1. Technical Field
The present invention generally relates to occlusion detection in images, and more particularly to systems and methods to report whether a particular object in a scene is occluded or not by using a rule-based combination of a hierarchy of visual classifiers.
2. Description of the Related Art
Existing computer vision systems often handle occlusion as a source of noise, rather than a positive source of information. The reason is that occlusion often causes computer vision methods to fail. More specifically, methods like visual object detection, tracking, and recognition, are sensitive to occlusions.
Most available systems do not report whether an object in a scene is occluded or not. They are generally designed to perform visual tasks (like tracking, detection, and recognition) under the presence of occlusion (e.g., using techniques based on robust statistics or subspace learning).
In general, these methods cope only with partial occlusion, and not with full occlusion. For example, there are systems for face detection that use multiple classifiers for different facial features, like nose, mouth, etc. and then verify their spatial arrangement relationship (e.g., eyes are always above mouth) for detection. However, these methods cannot handle the case where the face is fully occluded by a helmet, for example. The idea of combining multiple classifiers has been used to enhance visual object detection and recognition (e.g., combining a skin-color classifier with an appearance-based classifier to improve face detection). However, these techniques can only detect or recognize objects in the scene, but do not have the capability of reporting whether they are occluded or not.
There has been very little attention in the literature to detect masked persons in surveillance systems. In one such instance, for mask detection technology for occluded face analysis in a surveillance system, Gabor filters and the spatial arrangement of facial features are employed to detect whether a face is occluded or not. However, this approach fails when the face is fully occluded, which is a major problem for surveillance systems.